A survey on energy efficiency in information systems

Concerns about energy and sustainability are growing everyday involving a wide range of fields. Even Information Systems (ISs) are being influenced by the issue of reducing pollution and energy consumption and new fields are rising dealing with this topic. One of these fields is Green Information Technology (IT), which deals with energy efficiency with a focus on IT. Researchers have faced this problem according to several points of view. The purpose of this paper is to understand the trends and the future development of Green IT by analyzing the state-of-the-art and classifying existing approaches to understand which are the components that have an impact on energy efficiency in ISs and how this impact can be reduced. At first, we explore some guidelines that can help to understand the efficiency level of an organization and of an IS. Then, we discuss measurement and estimation of energy efficiency and identify which are the components that mainly contribute to energy waste and how it is possible to improve energy efficiency, both at the hardware and at the software level

Extracting Large Scale Spatio-Temporal Descriptions from Social Media

The ability to track large-scale events as they happen is essential for understanding them and coordinating reactions in an appropriate and timely manner. This is true, for example, in emergency management and decision-making support, where the constraints on both quality and latency of the extracted information can be stringent. In some contexts, real-time and large-scale sensor data and forecasts may be available. We are exploring the hypothesis that this kind of data can be augmented with the ingestion of semistructured data sources, like social media. Social media can diffuse valuable knowledge, such as direct witness or expert opinions, while their noisy nature makes them not trivial to manage. This knowledge can be used to complement and confirm other spatio-temporal descriptions of events, highlighting previously unseen or undervalued aspects. The critical aspects of this investigation, such as event sensing, multilingualism, selection of visual evidence, and geolocation, are currently being studied as a foundation for a unified spatio-temporal representation of multi-modal descriptions. The paper presents, together with an introduction on the topics, the work done so far on this line of research, also presenting case studies relevant to the posed challenges, focusing on emergencies caused by natural disasters

Knowledge graph embedding for experimental uncertainty estimation

Purpose: Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments. Design/methodology/approach: This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study. Findings: The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata. Originality/value: The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments

Object migration in temporal object-oriented databases

The paper presents T-ORM (Temporal Objects with Roles Model), an object-oriented data model based on the concepts of class and role. In order to represent the evolution of real-world entities, T-ORM allows objects to change state, roles and class in their lifetime. In particular, it handles structural and behavioral changes that occur in objects when they migrate from a given class to another. First, the paper introduces the basic features of the T-ORM data model, emphasizing those related to object migration. Then, it presents the query and manipulation languages associated with T-ORM, focusing on the treatment of the temporal aspects of object evolution

A Service-Based Framework for Flexible Business Processes

This article describes a framework for the design and enactment of flexible and adaptive business processes. It combines design-time and run-time mechanisms to offer a single integrated solution. The design-time environment supports the specification of process-drivenWeb applications with Quality of Service (QoS) constraints and monitoring annotations. The runtime identifies the actual services, from the QoS perspective, oversees the execution through monitoring, and reacts to failures and infringement of QoS constraints. The article also discusses these issues on a proof of concept application developed for an industrial supply chain scenario

Scalar field localization on a brane with cosmological constant

We address the localization of a scalar field, whose bulk-mass M is considered in a wide range including the tachyonic region,on a three-brane. The brane with non-zero cosmological constant $\lambda$ is embedded in five dimensional bulk space. We find in this case that the trapped scalar could have mass $m$ which has an upper bound and expressed as $m^2=m_0^2+\alpha M^2\leq \beta |\lambda|$ with the calculable numbers $m_0^2, \alpha, \beta$. We point out that this result would be important to study the stability of the brane and cosmological problems based on the brane-world.Comment: 14 pages, 5 figure

Stable de Sitter Vacua in 4 Dimensional Supergravity Originating from 5 Dimensions

The five dimensional stable de Sitter ground states in N=2 supergravity obtained by gauging SO(1,1) symmetry of the real symmetric scalar manifold (in particular a generic Jordan family manifold of the vector multiplets) simultaneously with a subgroup R_s of the R-symmetry group descend to four dimensional de Sitter ground states under certain conditions. First, the holomorphic section in four dimensions has to be chosen carefully by using the symplectic freedom in four dimensions; and second, a group contraction is necessary to bring the potential into a desired form. Under these conditions, stable de Sitter vacua can be obtained in dimensionally reduced theories (from 5D to 4D) if the semi-direct product of SO(1,1) with R^(1,1) together with a simultaneous R_s is gauged. We review the stable de Sitter vacua in four dimensions found in earlier literature for N=2 Yang-Mills Einstein supergravity with SO(2,1) x R_s gauge group in a symplectic basis that comes naturally after dimensional reduction. Although this particular gauge group does not descend directly from five dimensions, we show that, its contraction does. Hence, two different theories overlap in certain limits. Examples of stable de Sitter vacua are given for the cases: (i) R_s=U(1)_R, (ii) R_s=SU(2)_R, (iii) N=2 Yang-Mills/Einstein Supergravity theory coupled to a universal hypermultiplet. We conclude with a discussion regarding the extension of our results to supergravity theories with more general homogeneous scalar manifolds.Comment: 54 page

AdS spacetimes from wrapped M5 branes

We derive a complete geometrical characterisation of a large class of $AdS_3$, $AdS_4$ and $AdS_5$ supersymmetric spacetimes in eleven-dimensional supergravity using G-structures. These are obtained as special cases of a class of supersymmetric $\mathbb{R}^{1,1}$, $\mathbb{R}^{1,2}$ and $\mathbb{R}^{1,3}$ geometries, naturally associated to M5-branes wrapping calibrated cycles in manifolds with $G_2$, SU(3) or SU(2) holonomy. Specifically, the latter class is defined by requiring that the Killing spinors satisfy the same set of projection conditions as for wrapped probe branes, and that there is no electric flux. We show how the R-symmetries of the dual field theories appear as isometries of the general AdS geometries. We also show how known solutions previously constructed in gauged supergravity satisfy our more general G-structure conditions, demonstrate that our conditions for half-BPS $AdS_5$ geometries are precisely those of Lin, Lunin and Maldacena, and construct some new singular solutions.Comment: 1+56 pages, LaTeX; v2, references added; v3, minor corrections, final version to appear in JHE

The free energy in a magnetic field and the universal scaling equation of state for the three-dimensional Ising model

We have substantially extended the high-temperature and low-magnetic-field (and the related low-temperature and high-magnetic-field) bivariate expansions of the free energy for the conventional three-dimensional Ising model and for a variety of other spin systems generally assumed to belong to the same critical universality class. In particular, we have also derived the analogous expansions for the Ising models with spin s=1,3/2,.. and for the lattice euclidean scalar field theory with quartic self-interaction, on the simple cubic and the body-centered cubic lattices. Our bivariate high-temperature expansions, which extend through K^24, enable us to compute, through the same order, all higher derivatives of the free energy with respect to the field, namely all higher susceptibilities. These data make more accurate checks possible, in critical conditions, both of the scaling and the universality properties with respect to the lattice and the interaction structure and also help to improve an approximate parametric representation of the critical equation of state for the three-dimensional Ising model universality class.Comment: 22 pages, 10 figure

Algebraic Bethe ansatz approach for the one-dimensional Hubbard model

We formulate in terms of the quantum inverse scattering method the algebraic Bethe ansatz solution of the one-dimensional Hubbard model. The method developed is based on a new set of commutation relations which encodes a hidden symmetry of 6-vertex type.Comment: appendix additioned with Boltzmann weigths and R-matrix. Version to be published in J.Phys.A:math.Gen. (1997